Because of advances made in liquid crystal display (LCD) technology, there is a recent trend of substituting liquid crystal displays for conventional cathode ray tube displays. As the trend occurs with monitors and televisions, it affects both the computer and entertainment markets. Liquid crystal displays, however, may exhibit motion blur in moving images displayed on the LCD. Motion blur may be a problem for various reasons, such as liquid crystal response speeds, methods of device driving, light sources for the displays, and eye tracking characteristics.
There are cause-specific approaches to reduce motion blur that might otherwise be found in moving images, such as by using high-speed liquid crystal materials, liquid crystal over-drive techniques, or scanning backlights. Nevertheless, motion blur caused by eye tracking characteristics remains unsolved due at least in part to the hold-type nature of liquid crystal displays.
Eye tracking characteristics refers to low-pass filtering that is typical of a human being tracking a moving target. In other words, a human being tracking a moving target performs a mathematical integration of the locus of the moving target to maintain image continuity. Before performing the mathematical integration, however, the locus weight of the moving target is multiplied by luminance.
FIG. 1 illustrates 100 motion blur of a moving target 110 that is caused by human eye tracking characteristics. The vertical axis on the left of the figure represents location in pixels; the horizontal axis represents time in halves of one frame period; the moving target 110 has the white pixel value denoted by 1 and the background has the pixel value of 0. In this example, the moving target 110 moves downward at a unit speed of 1/60 second (e.g., one frame period). Owing to eye tracking characteristics, the human eye perceives an image in the first region 120 and second region 130 to bear values ranging from 0 to 1, as opposed to the default background pixel value, which results in the motion blur depicted in FIG. 2.
FIG. 3 illustrates the effect of eye tracking characteristics on a user's perception of an image. For example, an input signal 310 is received by a liquid crystal display 320 and is subjected to mathematical integration by an eye tracking characteristic model 330 before being turned into a user's perceived image 340. Although the input signal 310 may be restored by the liquid crystal display 320, the user's perceived image 340 may still not be free from motion blur. Hence, there is a continuing need for ways to cope with motion blur caused by eye tracking characteristics.